This course will cover
essentials of computer vision. We will learn basic principles of image
formation, image processing algorithms and different algorithms for
3D reconstruction and recognition from single or multiple
images (video). Apllications to 3D modelling, video analysis,
video surveillance, object recognition and vision based control will
This course is of interest to anyone seeking to process images or camera information, or to acquire a general background in issues related to real-world perception, image processing, object and scene recognition and multi-view geometry
Grading Homeworks (about every 2 weeks) 40% Midterm:
30% Final exam: 30%
Prerequisites linear algebra, calculus
Lecture Materials Lecture slides, lecture notes provided by instructor
 Invitation to 3D Vision: From Images to Geometric Models: Y. Ma, S. Soatto, J. Kosecka and S. Sastry web site
 Computer Vision: A Modern Approach: D. Forsythe and J. Ponce, Prentice-Hall, 2003
 Computer Vision: Algorithms and Applications. R. Szeliski, 2010, Springer online version of the book
 Image Processing, Analysis, and Machine Vision. Sonka, Hlavac, and Boyle. Thomson.
 Computer Vision. Ballard and Brown web site
Matlab, OpenCV. Homeworks will require using Matlab and OpenCV. You can buy a student version of Matlab in Johnson center or use it remotely from ITE labs. OpenCV is an C/C++ open source computer vision library.
Basic knowledge of image formation process
Basic knowledge of image processing techniques for color and gray level images: edge detection, corner detection, segmentation
Basics of video processing, motion computation and 3D vision and geometry
Ability to implement basic vision algorithms in Matlab and use OpenCV (open source computer vision library)
Ability to apply the appropriate technique to a problem, write a project report and present the results in class.